Question

During the lecture we talked about how a CAPM-based regression equation for IBM excess returns could be written as R

IBM=α+βᵢBₘ⋅Rₘ+e. Assume you used 2-years of historical monthly data to estimate the alpha and beta. Assume the estimated alpha is .15 and the beta is 1.2 . Assume the standard deviation in IBM excess returns is 32 and the standard deviation in market excess return is .20. What would the r-square value be from the regression? (Hint: Slide #23 in the Regression PowerPoint file talks about this.)
a) 0.5625
b) .7500
c) .4375
d) Can't answer

Answer 1

The R-squared value from the regression of IBM excess returns on market excess returns using a CAPM-based regression equation is 0.5625, explaining 56.25% of the variation.

Step-by-step explanation:

The R-squared value from the regression would be 0.5625, indicating that 56.25% of the variation in IBM excess returns can be explained by the variation in market excess returns. The coefficient of determination, R-squared, represents the percentage of variation in the dependent variable that can be explained by the independent variable using the regression equation. In this case, the variation in market excess returns is the independent variable, while IBM excess returns are the dependent variable.

The R-squared value from the regression would be 0.5625, indicating that 56.25% of the variation in IBM excess returns can be explained by the variation in market excess returns. The R-squared value, also known as the coefficient of determination, represents the percentage of variation in the dependent variable (IBM excess returns) that can be explained by the independent variable (market excess returns) using the regression equation.